Depression and associated factors in medical students in Acapulco during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Depression is common in medical students and the Mexican state of Guerrero has the highest rates of depression in the country. Acapulco, the seat of the state medical school, is a tourist destination that experienced early high rates of COVID-19. The COVID-19 pandemic closed all schools in Mexico, obliging a shift from face-to-face to virtual education. In this new context, medical students faced challenges of online teaching including inadequate connectivity and access technologies. Prolonged isolation during the pandemic may have had additional mental health implications.Assess depression prevalence and its associated factors affecting medical students in Acapulco, Mexico during the COVID-19 pandemic.A cross-sectional survey of students of the Faculty of Medicine of the Universidad Autónoma de Guerrero, in November 2020. After informed consent, students completed a self-administered questionnaire collating socio-demographic, academic and clinical variables, major life events and changes in mood. The Beck inventory provided an assessment of depression. Bivariate and multivariate analyses relied on the Mantel-Haenszel procedure to identify factors associated with depression. We estimated the odds ratio (OR) and 95% confidence intervals.33.8% (435/1288) of student questionnaires showed evidence of depression in the two weeks prior to the study, with 39.9% (326/817) of young women affected. Factors associated with depression included female sex (OR 1.95; 95%CI 1.48-2.60), age 18-20 years (OR 1.36; 95%CI 1.05-1.77), perceived academic performance (OR 2.97; 95%CI 2.16-4.08), perceived economic hardship (OR 2.18; 95%CI 1.57-3.02), and a family history of depression (OR 1.85; 95%CI 10.35-2.54). Covid-19 specific factors included a life event during the pandemic (OR 1.99; 95%CI 1.54-2.59), connectivity problems during virtual classes and difficulties accessing teaching materials (OR 1.75; 95%CI 1.33-2.30).The high risk of depression in medical students during the COVID-19 pandemic was associated with perceived academic performance and technical barriers to distance learning, in addition to known individual and family factors. This evidence may be useful for the improvement of programs on prevention and control of depression in university students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".